Constructing reports using metric-attribute combinations

ABSTRACT

Report construction techniques are disclosed. A first data set is received. The first data set includes a plurality of tables and a plurality of keys. One or more metric-attribute combinations is identified in the first data set. One or more dashboard reports is generated based on prioritized metric-attribute combinations from the first data set.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/068,531 entitled AUTOMATIC DATA WAREHOUSE GENERATION filed Mar.7, 2008 which is incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Traditional data mining and profiling tools require specialized trainingand education to use effectively. One reason is that a great deal ofstatistical information is generally returned to a user, and it isdifficult for a lay person to determine which information is potentiallyof interest and what the various statistics determined for the dataimply about the data.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 illustrates an example of an environment in which data warehousegeneration is performed.

FIG. 2 is a flow chart illustrating an embodiment of a process forgenerating a data warehouse.

FIG. 3 illustrates an example of a process for setting up metadata.

FIG. 4 illustrates an example of a process for setting up metadata.

FIG. 5 illustrates an example of a table that identifies twenty productsgrouped into four product categories.

FIG. 6 illustrates an example of a portion of a table that identifies100 customers, including age and gender information.

FIG. 7 illustrates an example of a portion of a table that identifies100 grocery stores of three times across twelve cities.

FIG. 8 illustrates an example of a portion of a table that identifies3,000 shopping baskets.

FIG. 9 illustrates an example of a portion line item details for theshopping baskets.

FIG. 10A illustrates an example of an interface.

FIG. 10B illustrates an example of an interface.

FIG. 10C illustrates an example of an interface.

FIG. 11A illustrates an example of an interface.

FIG. 11B illustrates an example of an interface.

FIG. 12 illustrates an example of a list of column properties.

FIG. 13 illustrates an example of an interface.

FIG. 14A illustrates an example of reporting data.

FIG. 14B illustrates an example of reporting data.

FIG. 14C illustrates an example of reporting data.

FIG. 15 is a flow chart illustrating an embodiment of a process forconstructing a set of reports.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Techniques for automatically generating a database schema (e.g., for arelational database) and data loading procedures for a data warehouseusing metadata derived from source data files are described herein. Adata warehouse is a repository of all or significant portions of datacollected by entities such as the various business systems of anenterprise. With a data warehouse, business data captured from diversesources can be used for subsequent analysis and access, e.g., bybusiness users.

Typically, analysis of such data requires the ability to look at thatdata at multiple levels. Analysis is also comparative, and requires theability to look at various measures for different parts of a business tocompare them vs. each other and over time. This process of hierarchicalmeasurement and comparison naturally leads to a “dimensional model.” Thedimensional metaphor provides a way of describing various hierarchicalmeasurements. In the dimensional model, two concepts include measuresand dimensions. Measures are measurements of data such as business data.Examples of measures include revenue, sales, assets, number of orders,etc. Measures can be analyzed across dimensions. Dimensions are ways ofgrouping measurements. Examples of dimensions include years, months,product categories, sales regions, etc. One example of information thatcan be expressed using a dimensional is the monthly sales by product.

A database schema defines the structure of a database system, describedin a formal language supported by a database management system (DBMS).In a relational database, the schema defines the tables, the fields ineach table, and the relationships between fields and tables.

A star schema is an example of the database schema for a dimensionalmodel. In the star schema design, a single object (the fact table) sitsin the middle and is connected to other surrounding objects (dimensiontables). A star schema can be simple or complex. A simple star mightinclude as little as one fact table; a more complex star has multiplefact tables. The star schema (also referred to as a star join schema) isan example of a data warehouse schema, and includes fact tablesreferencing any number of dimension tables. The “facts” that the datawarehouse helps analyze are classified along different “dimensions.” Thefact tables typically hold the main data, measures, while the dimensiontables describe each value of a dimension and can be joined to facttables as needed.

FIG. 1 illustrates an example of an environment in which data warehousegeneration is performed. In the example shown, system 106 receivessource data 104, such as from client 102. In various embodiments, client102 sends source data 104 files directly to system 106, e.g. byuploading source data 104 via a graphical or other interface. Client 102can also provide a pointer to where the data can be found, such as a URLand could also use a third party service to provide the source data tosystem 106.

As described in more detail below, data warehouse generation engine 110reads the received source data files, reads metadata stored in metadatarepository 112, and automatically generates a set of schema and dataloading procedures. Also as described in more detail below, dashboardengine 118 is configured to automatically evaluate the source data(and/or data in data warehouse 114) and to generate applicable reports.

Examples of different types of source data files 102 include flat files,Excel spreadsheets, tables in a relational database, structured datafiles such as XML data, and data obtained via a service call to anotherprogram (for example a web service or a remote procedure call). In thecase of flat files, fields from each record may have fixed width withpadding, or may be delimited by whitespace, tabs, commas or othercharacters. Source data may originate from a set of operational datastores, back office systems or existing data warehouses/marts.

Metadata in repository 110 is implemented using the Extensible MarkupLanguage (XML) and can also make use of any other suitable language. Insome embodiments, the metadata for the data warehouse schema and dataloading procedures is editable using Metadata Management Tool 120.Augmentations include but are not limited to adding column and tabletransformations.

Data Warehouse 114, which is implemented in this example by a relationaldatabase 116, is organized around dimensions and measures (or facts).The relational database is a collection of data items organized as a setof tables with each table organized into columns and rows. The schema ofdata warehouse 114 comprises staging tables mapped to source data files,dimension and measure tables mapped to staging tables, and joinrelationships between dimension and dimension or measure tables. Invarious embodiments it also contains information about the granularityof data, how data may be partitioned for performance purposes, and/orhow that data may be indexed by the underlying relational database forperformance purposes.

The creation and population of the relational database 116, based on thedata warehouse schema defined in repository 112 may be performed in anysuitable manner, as applicable. For example, in one embodiment, datawarehouse generation engine 110 interprets the logical dimensionalstructure in repository 112, automatically initializes correspondingrelational database 116, and loads data from source data 104 intodatabase 116 using data loading procedures in repository 112, by callingone or more APIs (e.g. in the SQL language).

As described in more detail below, using editing tool 120, a user isable to declare the grain of a source data file and label each sourcedata element as pertaining to a logical dimension and/or as a measure.Key columns are mapped by the user to their corresponding levels in thedimensional hierarchies. For example, a user may map the key column“Region_ID” in a “Business” dimension to a “Region” level in anassociated “Business” hierarchy. The corresponding data warehouse star,including staging, measure, and dimension tables and joins, as well asloading procedures from source files to staging and staging to warehousetables are then generated automatically. Metadata repository 112 storesall the metadata that defines the star schema, mapping andtransformations of source data files to staging tables, and stagingtables to measures and dimension tables. Also as described in moredetail below, in some embodiments system 106 automatically performsportions of this processing, reducing and in some cases eliminating theneed for a user to perform such tasks.

In various embodiments, the infrastructure provided by portions ofsystem 106 is located on and/or replicated across a plurality of serversrather than the entirety of system 106 being collocated on a singleplatform. Such may be the case, for example, if the contents of database116 are vast (and thus distributed across multiple databases) and/or ifsystem 106 is used to provide services to many users. Whenever system106 performs a task (such as receiving information from a user,producing reports, etc.), either a single component or a subset ofcomponents or all components of system 106 may cooperate to perform thetask.

FIG. 2 is a flow chart illustrating an embodiment of a process forgenerating a data warehouse. In some embodiments the process shown inFIG. 2 is performed by system 106. The process begins at 202 when datais received. For example, at 202, client 102 uploads source data 104 viaa web form provided by communication interface 108 to users that haveregistered for Internet-accessible accounts on system 106.

At 204, metadata is set up. As described in more detail below, portionsof the processing may be performed manually (e.g., by a user or withuser input) and portions of the processing may also be performedautomatically. Metadata is stored in a repository such as metadatarepository 112. For each table received at 202, two pieces of metadataare created. The grain of each table, describing the set of levels thatare appropriate for each file, is determined. For each column in thosefiles, a target dimension and level, and whether the column is measure,is also determined. Finally, a set of hierarchies are created, andcertain levels are selected to be turned into dimension tables.

At 206, metadata that defines a corresponding schema is generated, as isthe database schema. First, all levels of all dimensions that aredesignated to be dimension tables are evaluated. For each level, eachsource staging table is examined. If the table has a grain in thedimension of the appropriate level and the level of that grain is at orabove the level in question, the table is a candidate. All columns arescanned to determine which could be sources for that target dimensiontable. Once all source tables are scanned, each dimension table iscreated based on all the columns from all source files that are targetedto that level. All source tables are similarly scanned to determinewhich columns are measures and a measure table is generated for eachgrain.

At 208, the warehouse is loaded. First, the system scans source tablesin order to move data from those tables into target dimension tables.All source records are marked with a checksum before loading (so thatonly changed records will be moved). The system generates inserts intothe target dimension table for each staging table that is a source.Additional staging tables are joined in to supply related columns not inthat table (e.g., higher level keys). Higher level dimension tables alsoserve as lookups for keys. Levels are loaded in descending order. If thedimension table is type I (i.e., only current versions of each dimensionrecord are kept), then the system does an update for any non-inserteddimensional records that may have changed attributes. If the dimensiontable is type II (i.e., history is kept), dimension records that havechanged are flagged as retired, and inserts add the new records with thechanged attributes.

Next, the system scans source tables to find sources for all measuregrains. In the case of snapshots, old records may be truncated, asapplicable. In the case of inserts, higher level surrogate keys arelooked up from the dimension tables. Natural keys—which may be composedof multiple columns—are transformed into simple integer surrogate keysfor performance.

FIG. 3 illustrates an example of a process for setting up metadata. Insome embodiments the processing shown in FIG. 3 is performed at portions202 and 204 in the process shown in FIG. 2. The process begins at 302when a user defines logical dimensions and associated dimensionalhierarchies, such as by using metadata editor 120. The dimensionalhierarchies are specified through a user interface (UI) that can takeany suitable form (e.g. a graphical interface, a command line, aworld-wide-web interface accessible by a web browser, or any other inputmechanism).

At 304, source data files are read by data warehouse generation engine110 and a definition of each source file is automatically generated inmetadata repository 112. The definition of a source file includesinformation such as format type, name and data type for each column andmay be modified by the user via editor 120.

At 306, corresponding definitions of staging tables are automaticallycreated in metadata repository 112 based on source data filerepresentations. Based on a user's input, more than one staging tablemay be defined based on a single source data file. A user may modify thestaging table definitions using metadata editor 120. Staging tablesserve as a temporary holding or staging place for data when it isbrought into a relational database for the first time.

At 308, the user declares the grain of each staging table using themetadata editor 120. The grain is declared as a reference to at leastone logical dimension and a level in the associated dimensionalhierarchy.

At 310, the user declares each column in a staging table to be a sourceto a dimension, to a fact/measure (or to both simultaneously) usingmetadata editor 120. A user may define table transformations on astaging table and column transformations for columns in a staging table.These transformations allow data in a staging table to bealtered/modified before it is moved to its ultimate destination, thedimension and measure tables.

FIG. 4 illustrates an example of a process for setting up metadata. Insome embodiments the processing shown in FIG. 4 is performed at portion204 in the process shown in FIG. 2. The process begins at 402 whensystem 106 determines, automatically, for each row in each table, whatthe most appropriate key is. In some cases the key—known as a naturalkey—is provided in a single column. In other cases, a combination ofcolumns may need to be combined to serve as a key. In this example, theresulting key is referred to as a synthetic key. If a synthetic key isneeded, in some embodiments system 106 weights the data types of thecolumns it evaluates for use in the synthetic key. For example, system106 is in some embodiments configured to prefer integers over floats. Ifneither a natural key nor a synthetic key can be found, in someembodiments a key is generated and included as an additional column inthe data.

The data provided at portion 202 of the process shown in FIG. 2 istypically provided in a flat form. The target data structures to bebuilt by system 106 are analytical ones based on notions of adimensional structure, such as cubes, hypercubes, etc. Accordingly, oncethe keys have been determined at 402, system 106 attempts to assemblethe information into a dynamical hierarchal structure.

At 404, each of the columns in the source data is classified as either ameasure, a dimension, or both. By default, system 106 classifies columnsas both a measure (a source for metrics) and a dimension (a source fordimensional attributes). Typically, columns populated with numericvalues are classified as both measures and dimensions, and columnspopulated with text strings are classified only as dimensions.

At 406, joins are determined. For example, two columns with the samename and same data type are considered to be the same column and can belinked.

At 408, system 106, for every level of data in each dimension, creates adimension table that stores dimensional attributes. System 106 thenevaluates the source tables to determine which metrics need to begenerated, groups them into grains, and for each grain creates a facttable.

At 410, hierarchies are determined and combined. For example, at 410 itis determined that a particular dimension table for geography at theregion level can be fed from a particular table and that geography atthe city level is a “lower” level in the hierarchy.

Example Shopping Scenario

In the following example, suppose an analyst (hereinafter “Alice”) worksfor a grocery store chain and has been asked to analyze checkout datacollected from a sample of markets in North America over the course ofone year. As described in the following example, Alice wishes toidentify sales patterns and information of interest and to present a setof dashboards to the grocery store chain's managers. Alice contactssystem 106 via client 102 and uploads sales data in the form of aMICROSOFT EXCEL file that includes five spreadsheets. Alice may interactwith system 106 in either an “automatic” mode (in which the bulk of theprocessing is performed automatically by system 106, such as by usingthe process of FIG. 4), or in an advanced mode; described below.

FIG. 5 illustrates an example of a table that identifies twenty productsgrouped into four product categories. FIG. 6 illustrates an example of aportion of a table that identifies 100 customers, including age andgender information. FIG. 7 illustrates an example of a portion of atable that identifies 100 grocery stores of three times across twelvecities. FIG. 8 illustrates an example of a portion of a table thatidentifies 3,000 shopping baskets. FIG. 9 illustrates an example of aportion line item details for the shopping baskets.

FIG. 10A illustrates an example of an interface. When Alice uploads hersource data to system 106, system 106 scans the data in the spreadsheetsto identify column types and the relationships between the sheets. Aliceis then presented with the interface shown.

In order to process source data, its grain needs to be defined first.The grain of a data source defines what a data record in the sourcerepresents. It specifies the level of detail captured in a source. Insystem 106, the grain is declared as a combination of hierarchy levels.System 106 establishes relationships between multiple sources based onkey columns with identical names and the grains of the sources.

Alice can define the grain of the “Cart Details” data source byselecting region 1002 of the interface shown in FIG. 10A. She creates anew hierarchy, known as “Sales.” The “Card ID” and “Product ID” columnsof the “Cart Details” table uniquely identify records in the CartDetails table and form a “Product Sale” level in the Sales hierarchy.

FIG. 10B illustrates an example of an interface. When Alice is finishedmaking changes to the “Cart Details” definition, she is presented withthe interface shown in FIG. 10B.

Analogous to the actions taken by Alice in conjunction with FIGS. 10Aand 10B, Alice takes the following actions: For the “Customers” datasource, she creates a new hierarchy named “Customers” with a singlelevel “Customer” mapped to the column “Loyalty Card ID.” For the“Products” data source, she creates a new hierarchy named “Products”with two levels, one level “Product” mapped to “Product ID” and a levelabove it named “Category” mapped to “Category ID.” For the “ShoppingCarts” data source, she modifies the existing “Sales” hierarchy byadding a level “Cart” mapped to “Cart ID” above the existing “ProductSale” level. For the “Stores” data source, she creates a new hierarchynamed “Stores” with a single level “Store” mapped to “Store ID.”

FIG. 10C illustrates an example of an interface. When Alice is finishedmaking changes to the above definitions, she is presented with theinterface shown in FIG. 10C.

Once the hierarchies and levels are in place, Alice uses the interfaceshown in FIG. 11A to finalize the grain definition for some sources. For“Cart Details” she adds the following three hierarchy-level combinationsto the grain definition by selecting it from the two drop downs in the“Define Grain” section for the source: “Products-Product,”“Customers-Customer,” and “Stores-Store.” The second two are added tothe grain definition to force system 106 to create a “Sales Date”version of the “Quantity” measure.

Alice then uses the interface shown in FIG. 11B to remove theautomatically added hierarchy-level combination “Sales-Product Sale”from “Shopping Carts.” Next, she adds “Sales-Cart,”“Customers-Customer,” and “Stores-Store” to the grain definition.

Before proceeding to processing the data sources, the properties of allcolumns in a data source should be defined. Alice makes the followingadjustments: For the data source “Cart Details,” she indicates that the“Cart ID” and “Product ID” columns are needed as measures. She sets thehierarchy of the “Quantity” column to “Sales” and makes the level blank.For the remaining sources, she sets the column properties as shown inFIG. 12.

Next, Alice indicates to system 106 that the grocery data should beprocessed, through the interface shown in FIG. 13. System 106 allowsusers to upload new snapshots of the same data sources over time. Eachtime a new snapshot is uploaded, it is tagged with a unique load ID anddate. The load date is used for time-dependent analysis. When Aliceopens the Process Data page shown in FIG. 13, system 106 sets the loaddate by default to the current date.

After successfully processing the uploaded data, Alice is now ready toanalyze the data and build reports. She begins by building a simplereport that breaks out quantity sold by each product category usingtools provided by system 106. First, she opts to view the quantity ofproducts sold by category and is presented with the report shown in FIG.14A.

Alice may share the report with others, e.g. via interface 108. Alicecan also manipulate the report. For example, Alice can indicate tosystem 106 that she would like to see “Year/Month” data in addition to“Category Name” and “Quantity.”

As shown in FIG. 14B, the resulting table shows a single value for“Year/Month” across all rows. The existing “Quantity” measure willreport the quantity sold in relation to the load date. Since Alice hasso far only loaded her data a single time, only one load date exists andtherefore “Year/Month” is single valued. In order to report the quantitysold in relation to the sales date, system 106 provides another versionof the “Quantity” measure starting with the prefix “Sales Date.” To seeit, Alice removes the “Sum: Quantity” column from the report by clickingon the “Remove chart” icon and adds the measure “Sum” from the folderMeasures/Quantity/By Sales Date to the report. Since the datasetcontains sales data for all twelve months in 2007, instead of showing arow for each month of the year across all product categories, a pivottable can be used to display the report, as shown in FIG. 14C.

Additional Example

The following is yet another example of how a data warehouse can beautomatically generated. The first step is to map in source data files,for example, as explained above. One example of source data includeextracts of many tables in a relational database that tracks orders fora company for a given period of time. Another example, in the case of afinancial services company, would be all trades, transactions, accountsand other financial information generated in the normal course ofoperating the business. The files serve as sources for staging tables.Staging tables are places in a relational database where raw source datacan be loaded for further processing.

In some embodiments, an administrator initially sets up the generalhierarchies present in the data. For example, the data containsinformation on products and product categories. Each product category ismade up of several products. That relationship can be represented in aproduct hierarchy where the name of the hierarchy is Product, the toplevel is Category and the bottom level is Product. Similarly, if orderdata is received, some data will pertain to the entire order (likecustomer, etc.) and other data will pertain to each order line item. Anorder hierarchy can be erected with both levels. Using this hierarchy,the system can be instructed what granularity of data is in each stagingtable. As one example, an Orders staging table has an Order level of theOrder hierarchy selected as well as the Customer level of the Customerhierarchy and the Employee level of the employee hierarchy. That isbecause each order record has assigned order information, a specificcustomer and a specific employee—its grain. The order item table is onelevel deeper and contains information at the Order Item level of theOrder hierarchy.

Schema includes fact tables and dimension tables. Fact tables includemetrics to be aggregated and dimension tables include ways to group orslice these facts. Using the techniques described herein, fact tablesare automatically generated for each grain for which there areaggregateable metrics. In addition, by clicking on a checkbox for agiven level in a hierarchy, the system will automatically determine thetable format required to build a dimension table for that level. In someembodiments it does so by first determining which columns must bepresent in the dimension table at this level (by scanning all columns inall staging tables that are targeted to this level). Those columns andtheir required data types and formats as well as the keys for each leveldetermine what is required in that table. In addition, special types ofdimension tables can be supported (for example, like below, slowlychanging dimension versions of dimension tables that record history anddegenerate dimension tables that are stored in the fact tablesthemselves). All an administrator needs to do is specify the level,identify the column which uniquely defines that level (a level key) andthen check the box to have a dimension table created for that level.Once all levels in a dimension are specified, the system can determine,using the dimension targets in the staging tables as well as the grainlevels for each staging table what columns must be placed in whichinstantiated dimension table. Each column must be in a table at or belowthe level specified for that column.

In addition, metrics can be analyzed over time and there are variousways users might want to aggregate data over time. With the techniquesdescribed herein, the user can specify all the different kinds of timeaggregation or shifting that one desires and all the variants of themetrics that calculate these versions are generated automatically.

Once the previous have been, specified, an automatic schema generationtool can be run (i.e. the application can be “built”). When that happensdefinitions for many tables get created, all the columns in those tablesare created, keys are created into those tables for later access andjoin relationships based on the hierarchies are also setup. During anapplication build, previously automatically built items are removed andautomatically generated items are regenerated. Surrogate keys areautomatically generated and added to the hierarchies after a build. Inaddition, new metrics are created based on columns in the staging tables(using the grain of the staging table, data types, etc. to infermetrics). Additionally, individual physical mappings are also generated(each metric may exist in one or more physical tables—each at adifferent grain of aggregation).

Dimension columns are automatically created in each dimension. And,dimension columns are physically mapped to tables. Suppose there is adimension table for Order data and a dimension table for individualorder line item data. The system automatically determines based onsource table grain and hierarchies which columns should go where and howthey should be mapped.

Finally, once the schema is determined for the warehouse, load processesneed to be generated to populate this schema from source staging tables.Using the techniques herein, an analysis is performed of each targettable against every staging table and it is determined what can beloaded from where. It then automatically generates the data load queriesfor the database that load the target tables from the staging tables.

Quick Dashboards

In addition to providing the reporting tools described above, system 106also provides a dashboard engine 118 that assists users such as Alice inautomatically locating information that is likely to be of interest,such as to the grocery store managers. As described in more detailbelow, dashboard engine 118 is configured to evaluate the source dataprovided by Alice for metrics of interest, to locate metric-attributecombinations (such as number of products sold per region), and topopulate a dashboard with reports associated with the metric-attributecombinations that are likely to be of interest to Alice.

FIG. 15 is a flow chart illustrating an embodiment of a process forconstructing a set of reports. In some embodiments the process shown inFIG. 15 is performed by system 106.

The process begins at 1502 when source data is received. For example,when Alice uploads source data 1504 to system 106, both data warehousegeneration engine 110 and dashboard engine 118 may ingest the data atthe same time. In various embodiments, dashboard engine 118 receivesdata (including as realtime data) from data warehouse generation engine110, relational database 116, or another component of system 106, ratherthan receiving it at the time Alice provides the source data to thesystem. In various embodiments, dashboard engine 118 receives data froma third party and/or portions of system 106 such as the data warehousegeneration engine are omitted and the techniques described herein areadapted with respect to quick reports are used on other types of data ordata sets, as applicable.

At 1504, metric-attribute combinations are identified and at 1506 thecombinations are prioritized. Portions 1504 and portions 1506 can beperformed together, may be performed in either order, and may beperformed in a variety of ways. As one example, all of the metricsextracted from source data 104 by data warehouse generation engine 110may first be evaluated to determine which metrics have the least uniformstatistical distribution. Metric-attribution combinations include atleast one metric and at least one attribute, but may include additionalmetrics and/or attributes as well. One technique for determining whichmetrics have the least uniform distribution is kurtosis. Othertechniques may also be used, such as skewness.

The metrics showing the least uniform distribution (e.g., the top 10 outof 100 possible metrics) then have their associated attributesevaluated, again, to select the combinations of metrics and attributes(e.g., quantity per city) that are likely to be of interest to Alice.One technique for evaluating the candidate metric-attribute combinationsis through the use of decision trees. Generally, attributes with ahigher information gain relative to a chosen metric will likely be moreinteresting to a user than attributes having a lower information gain.Additional techniques for selecting the most significant attributesinclude the use of chi-squared automatic interaction detector,classification and regression trees, entropy, and any other applicablenode splitting techniques.

The metric-attribute combinations are ranked, e.g., based on informationgain, and the highest ranked combinations are used to generate reportsshowing such information as quantity sold per region. In variousembodiments, multiple reports are presented on the same screen, referredto herein as a dashboard. Alice can share access to the dashboard withothers users, and can also interact with the dashboard; e.g., by addingand removing additional attributes and/or metrics from inclusion in thereporting. For example, suppose one report selected for display to Aliceis products by region. In various embodiments, Alice is provided with aninterface that allows her to refine the report so that it shows productsby region by time.

Data Example

The following is an example of a log file generated by an embodiment ofsystem 106 when processing the data described in conjunction with FIGS.5-9.

2009-03-05 17:21:38,296-0800 [Pool Worker—9] INFO—Starting: Repositoryc:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\repository_dev.xml

2009-03-05 17:21:38,609-0800 [Pool Worker—9] INFO—Application Name:Grocery Sales/schst@example.com

2009-03-05 17:21:38,906-0800 [Pool Worker—9] INFO—Engine RepositoryVersion: 7, Repository Version: 7

2009-03-05 17:21:38,906-0800 [Pool Worker—9] DEBUG—File: CartDetails.txt, Encoding: UTF-8, Separator: |, Quote: “, IgnoredRows: 0,IgnoredLastRows: 0, HasHeaders: true, IgnoreBackslash: false

2009-03-05 17:21:38,906-0800 [Pool Worker—9] DEBUG—File: Customers.txt,Encoding: UTF-8, Separator: |, Quote: “, IgnoredRows: 0,IgnoredLastRows: 0, HasHeaders: true, IgnoreBackslash: false

2009-03-05 17:21:38,906-0800 [Pool Worker—9] DEBUG—File: Products.txt,Encoding: UTF-8, Separator: |, Quote: “, IgnoredRows: 0,IgnoredLastRows: 0, HasHeaders: true, IgnoreBackslash: false

2009-03-05 17:21:38,906-0800 [Pool Worker—9] DEBUG—File: ShoppingCarts.txt, Encoding: UTF-8, Separator: |, Quote: “, IgnoredRows: 0,IgnoredLastRows: 0, HasHeaders: true, IgnoreBackslash: false

2009-03-05 17:21:38,906-0800 [Pool Worker—9] DEBUG—File: Stores.txt,Encoding: UTF-8, Separator: |, Quote: “, IgnoredRows: 0,IgnoredLastRows: 0, HasHeaders: true, IgnoreBackslash: false

2009-03-05 17:21:38,937-0800 [Pool Worker—9] INFO—Database connectionpool created for Default Connection using connection stringjdbc:sqlserver://localhost;databaseName=AcomData and drivercom.microsoft.sqlserver.jdbc.SQLServerDriver

2009-03-05 17:21:39,437-0800 [Pool Worker—9] INFO—JDBC Driverinformation: Microsoft SQL Server 2005 JDBC Driver, 1.2.2828.100

2009-03-05 17:21:39,437-0800 [Pool Worker—9] INFO—JDBC Version: 3.0

2009-03-05 17:21:40,078-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Cart_Details, Cart_Details Shopping_Carts]

2009-03-05 17:21:40,140-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Shopping_Carts, Cart_Details Customers Customers]

2009-03-05 17:21:40,171-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Shopping_Carts, Cart_Details Stores Stores]

2009-03-05 17:21:40,203-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Cart_Details, Cart_Details Shopping_Carts, Cart_DetailsCustomers Customers]

2009-03-05 17:21:40,234-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Cart_Details, Cart_Details Shopping_Carts, Cart_DetailsStores Stores]

2009-03-05 17:21:40,343-0800 [Pool Worker—9] INFO—Creating snowflake:[Cart_Details Shopping_Carts, Cart_Details Customers Customers,Cart_Details Stores Stores]

2009-03-05 17:21:40,421-0800 [Pool Worker—9] INFO—Instantiating thecache: c:/SMI/Data/389a3dee-ce29-4e42-90ad-44970093f745/cache

2009-03-05 17:21:40,421-0800 [Pool Worker—9] DEBUG—Creating JDBM RecordManager:c:/SMI/Data/389a3dee-ce29-4e42-90ad-44970093f745/cache\cacheMaps

2009-03-05 17:21:40,500-0800 [Pool Worker—9] DEBUG—Creating JDBM HTreeInstance: cmap

2009-03-05 17:21:40,531-0800 [Pool Worker—9] DEBUG—Creating JDBM HTreeInstance: cfmap

2009-03-05 17:21:40,546-0800 [Pool Worker—9] DEBUG—Creating JDBM HTreeInstance: emap

2009-03-05 17:21:40,546-0800 [Pool Worker—9] DEBUG—Creating JDBM HTreeInstance: rmap

2009-03-05 17:21:40,562-0800 [Pool Worker—9] INFO—Repository VariableInitialization Started.

2009-03-05 17:21:40,562-0800 [Pool Worker—9] INFO—Variable: LoadNumber=1

2009-03-05 17:21:40,562-0800 [Pool Worker—9] INFO—Variable: LoadDate=5Mar. 2009

2009-03-05 17:21:40,562-0800 [Pool Worker—9] INFO—Repository VariableInitialization Complete.

2009-03-05 17:21:40,578-0800 [Pool Worker—9] INFO—Security Settings forPasswords: min: 6, max: 100, upper and lower case: false,non-alphanumeric: false, not contain the username: false

2009-03-05 17:21:40,578-0800 [Pool Worker—9] WARN—Create User Operation(Create User) does not exist

2009-03-05 17:21:40,578-0800 [Pool Worker—9] WARN—Delete User Operation(Delete User) does not exist

2009-03-05 17:21:40,578-0800 [Pool Worker—9] WARN—Disable User Operation(Disable User) does not exist

2009-03-05 17:21:40,578-0800 [Pool Worker—9] DEBUG—Connection allocatedfor thread (Thread[Pool Worker—9,5,main])

2009-03-05 17:21:40,656-0800 [Pool Worker—9] WARN—SMI_USERS either doesnot exist or does not have 2 [username, password] or 4 [username,password, fullname, email] columns; disabling DB-based authenticationand authorization

2009-03-05 17:21:40,656-0800 [Pool Worker—9] WARN—Problems encounteredwhen trying to create DatabaseRealm, using Repository only forauthentication and authorization

2009-03-05 17:21:40,781-0800 [Pool Worker—9] INFO—Performance Model:Default Performance Model: reference population does not exist: DefaultReference Population

2009-03-05 17:21:40,859-0800 [Pool Worker—9] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.TXN_COMMAND_HISTORY ([TM]DATETIME,[COMMAND_TYPE] VARCHAR(30),[STEP] VARCHAR(30),[SUBSTEP]VARCHAR(255),[ITERATION] VARCHAR(20),[STATUS] INTEGER,[NUMROWS]BIGINT,[NUMERRORS] BIGINT,[NUMWARNINGS] BIGINT,[MESSAGE] VARCHAR(1000))

2009-03-05 17:21:40,875-0800 [Pool Worker—9] DEBUG—CREATE INDEXDX_TXN_COMMAND_HISTORYITERATION ONS_N389a3dee_ce29_4e42_90ad_44970093f745.TXN_COMMAND_HISTORY (ITERATION)

2009-03-05 17:21:40,906-0800 [Pool Worker—9] INFO—Elapsed Time=0minutes, 2 seconds for: Repositoryc:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\repository_dev.xml

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Starting: SetVariableLoadDate 5 Mar. 2009

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Set repositoryvariable ‘LoadDate’ to value ‘5 Mar. 2009’

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 0 seconds for: SetVariable LoadDate 5 Mar. 2009

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Starting: ResetETLRun1

2009-03-05 17:21:41,328-0800 [Pool Worker—10] DEBUG—Clearing ETL historyfor 1

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Deleting previousdata from TXN_COMMAND_HISTORY

2009-03-05 17:21:41,328-0800 [Pool Worker—10] DEBUG—DELETE

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.TXN_COMMAND_HISTORY

WHERE ITERATION=‘1’ AND COMMAND_TYPE=‘ETL’

2009-03-05 17:21:41,328-0800 [Pool Worker—10] DEBUG—Connection allocatedfor thread (Thread[Pool Worker—10,5,main])

2009-03-05 17:21:41,328-0800 [Pool Worker—10] DEBUG—Deleted 0 rows

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 0 seconds for: ResetETLRun 1

2009-03-05 17:21:41,328-0800 [Pool Worker—10] INFO—Starting:GenerateSchema 1 notime

2009-03-05 17:21:41,359-0800 [Pool Worker—10] DEBUG—Logging stepGenerateSchema for 1, status Running

2009-03-05 17:21:41,359-0800 [Pool Worker—10] INFO—StartingGenerateSchema

2009-03-05 17:21:41,406-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS([Cart_ID$] INTEGER,[Store_ID$] INTEGER,[Loyalty_Card_ID$]INTEGER,[Sales_Date$] DATETIME,[Shopping_Carts_13875573$] INTEGERIDENTITY,[LOAD_ID] INTEGER,[ST_Cart_Details_CKSUM$]INTEGER,[ST_Shopping_Carts_CKSUM$] INTEGER)

2009-03-05 17:21:41,562-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS([Cart_ID$] INTEGER,[Product_ID$] INTEGER,[Quantity$]INTEGER,[Shopping_Carts_13875573$]INTEGER,[Cart_Details200351043$]INTEGER IDENTITY,[LOAD_ID] INTEGER,[ST_Cart_Details_CKSUM$] INTEGER)

2009-03-05 17:21:41,640-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS([Loyalty_Card_ID$] INTEGER,[Age_Group$] NVARCHAR(8),[Gender$]NVARCHAR(6),[Customers120094747$] INTEGER IDENTITY,[LOAD_ID]INTEGER,[ST_Customers_CKSUM$] INTEGER,[ST_Shopping_Carts_CKSUM$]INTEGER)

2009-03-05 17:21:41,750-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS([Product_ID$] INTEGER,[Category_ID$] INTEGER,[Product_Name$]NVARCHAR(10),[Category_Name$] NVARCHAR(9),[Products1249892458$] INTEGERIDENTITY,[LOAD_ID] INTEGER,[ST_Products_CKSUM$]INTEGER,[ST_Cart_Details_CKSUM$] INTEGER)

2009-03-05 17:21:41,859-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES ([Store_ID$]INTEGER,[Region$] NVARCHAR(5),[City$] NVARCHAR(15),[Type$]NVARCHAR(11),[Stores1543357431$] INTEGER IDENTITY,[LOAD_ID]INTEGER,[ST_Stores_CKSUM] INTEGER,[ST_Shopping_Carts_CKSUM$] INTEGER)

2009-03-05 17:21:41,921-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES([LOAD_ID] INTEGER,[Cart_Details$Cart_Details200351043$]INTEGER,[Cart_Details$Cart_ID$] INTEGER,[Cart_Details$Product_ID$]INTEGER,[Products$Products1249892458$] INTEGER,[Products$Product_ID$]INTEGER,[Customers$Customers120094747$]INTEGER,[Customers$Loyalty_Card_ID$] INTEGER,[Stores$Stores1543357431$]INTEGER,[Stores$Store_ID$]INTEGER,[Cart_Details$Shopping_Carts_13875573$] INTEGER,[Time$Day_ID$]INTEGER,[Time$Week_ID$] INTEGER,[Time$Month_ID$]INTEGER,[Time$Quarter_ID$] INTEGER, [Cart_ID$] INTEGER, [Product_ID$]INTEGER, [Quantity$] INTEGER,[Time$Sales_Date_Day_ID$] INTEGER)

2009-03-05 17:21:42,062-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES([LOAD_ID] INTEGER,[Cart_Details$Shopping_Carts_13875573$]INTEGER,[Cart_Details$Cart_ID$] INTEGER,[Customers$Customers120094747$]INTEGER,[Customers$Loyalty_Card_ID$] INTEGER,[Stores$Stores1543357431$]INTEGER,[Stores$Store_ID$] INTEGER,[Time$Day_ID$]INTEGER,[Time$Week_ID$] INTEGER,[Time$Month_ID$]INTEGER,[Time$Quarter_ID$] INTEGER,[Cart_ID$] INTEGER,[Loyalty_Card_ID$]INTEGER,[Store_IS$] INTEGER,[Time$Sales_Date_Day_ID$] INTEGER)

2009-03-05 17:21:42,140-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY ([LOAD_ID]INTEGER,[Products$Products1249892458$] INTEGER,[Products$Product_ID$]INTEGER,[Time$Day_ID$] INTEGER,[Time$Week_ID$] INTEGER,[Time$Month_ID$]INTEGER,[Time$Quarter_ID$] INTEGER,[Product_ID$] INTEGER,[Category_ID$]INTEGER,[Unit_Price$] FLOAT)

2009-03-05 17:21:42,234-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY ([LOAD_ID]INTEGER,[Customers$Customers120094747$]INTEGER,[Customers$Loyalty_Card_ID$] INTEGER,[Time$Day_ID$]INTEGER,[Time$Week_ID$] INTEGER,[Time$Month_ID$]INTEGER,[Time$Quarter_ID$] INTEGER,[Loyalty_Card_ID$] INTEGER)

2009-03-05 17:21:42,281-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY ([LOAD_ID]INTEGER,[Stores$Stores1543357431$] INTEGER,[Stores$Store_ID$]INTEGER,[Time$Day_ID$] INTEGER,[Time$Week_ID$] INTEGER,[Time$Month_ID$]INTEGER,[Time$Quarter_ID$] INTEGER,[Store_ID$] INTEGER)

2009-03-05 17:21:42,312-0800 [Pool Worker—10] DEBUG—Logging stepGenerateSchema for 1, status Complete

2009-03-05 17:21:42,312-0800 [Pool Worker—10] INFO—FinishedGenerateSchema

2009-03-05 17:21:42,312-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 0 seconds for: GenerateSchema 1 notime

2009-03-05 17:21:42,312-0800 [Pool Worker—10] INFO—Starting: LoadStagingc:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data 1 loadgroup=ACORNdatabasepath=c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\datanumrows=−1

2009-03-05 17:21:42,375-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN] for 1, status Running

2009-03-05 17:21:42,375-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN]

2009-03-05 17:21:42,375-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Cart_Details] for 1, status Running

2009-03-05 17:21:42,390-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN: ST_Cart_Details]

2009-03-05 17:21:42,390-0800 [Pool Worker—10] DEBUG—Deleting formatfiles that might be lingering over from previous unsuccessfulrunprematurely terminated—Cart Details.txt.format.

2009-03-05 17:21:42,390-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Cart Details.txt.format

2009-03-05 17:21:42,390-0800 [Pool Worker—10] DEBUG—Deleting tmp filesthat might be lingering over from previous unsuccessful runprematurelyterminated—Cart Details.txt.tmp.

2009-03-05 17:21:42,390-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Cart Details.txt.tmp

2009-03-05 17:21:42,390-0800 [Pool Worker—10] INFO—Preprocessing sourcefile Cart Details.txt(c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Cart Details.txt)

2009-03-05 17:21:42,609-0800 [Pool Worker—10] INFO—Read 12438 lines,wrote 12438 lines

2009-03-05 17:21:42,609-0800 [Pool Worker—10] INFO—Successfullypreprocessed source file Cart Details.txt

2009-03-05 17:21:42,656-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details ([Cart_ID$]Integer,[Product_ID$] Integer,[Quantity$]Integer,[DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$]INTEGER,[DW_DM_PRODUCTS_PRODUCTS_CKSUM$]INTEGER,[DW_DM_CART_DETAILS_CART_DETAILS_CKSUM$] INTEGER)

2009-03-05 17:21:42,734-0800 [Pool Worker—10] INFO—Bulk loading stagingtable ST_Cart_Details from source file Cart Details.txt

2009-03-05 17:21:42,734-0800 [Pool Worker—10] DEBUG—BULK INSERTS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details FROM‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\CartDetails.txt.tmp’ WITH(FORMATFILE=‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\CartDetails.txt.format’,FIRSTROW=1,ROWS_PER_BATCH=12438,DATAFILETYPE=‘widechar’)

2009-03-05 17:21:42,796-0800 [Pool Worker—10] INFO—Successfully loadedST_Cart_Details

2009-03-05 17:21:42,796-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Customers] for 1, status Running

2009-03-05 17:21:42,796-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN: ST_Customers]

2009-03-05 17:21:42,796-0800 [Pool Worker—10] DEBUG—Deleting formatfiles that might be lingering over from previous unsuccessfulrunprematurely terminated—Customers.txt.format.

2009-03-05 17:21:42,796-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Customers.txt.format

2009-03-05 17:21:42,796-0800 [Pool Worker—10] DEBUG—Deleting tmp filesthat might be lingering over from previous unsuccessful runprematurelyterminated—Customers.txt.tmp.

2009-03-05 17:21:42,796-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Customers.txt.tmp

2009-03-05 17:21:42,796-0800 [Pool Worker—10] INFO—Preprocessing sourcefile Customers.txt(c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Customers.txt).

2009-03-05 17:21:42,796-0800 [Pool Worker—10] INFO—Read 100 lines, wrote100 lines

2009-03-05 17:21:42,796-0800 [Pool Worker—10] INFO—Successfullypreprocessed source file Customers.txt

2009-03-05 17:21:42,859-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers ([Loyalty_Card_ID$]Integer,[Age_Group$] NVarchar(8),[Gender$]NVarchar(6),[DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$] INTEGER)

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Bulk loading stagingtable ST_Customers from source file Customers.txt

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—BULK INSERTS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers FROM‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Customers.txt.tmp’WITH (FORMATFILE=‘c:\SMI\Data \389a3dee-ce29-4e42-90ad-44970093f745\data\Customers.txt.format’,FIRSTROW=1,ROWS_PER_BATCH=100,DATAFILETYPE=‘widechar’)

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Successfully loadedST_Customers

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Products] for 1, status Running

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN: ST_Products]

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—Deleting formatfiles that might be lingering over from previous unsuccessfulrunprematurely terminated—Products.txt.format.

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Products.txt.format

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—Deleting tmp filesthat might be lingering over from previous unsuccessful runprematurelyterminated—Products.txt.tmp.

2009-03-05 17:21:42,921-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Products.txt.tmp

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Preprocessing sourcefile Products.txt(c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Products.txt)

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Read 20 lines, wrote20 lines

2009-03-05 17:21:42,921-0800 [Pool Worker—10] INFO—Successfullypreprocessed source file Products.txt

2009-03-05 17:21:42,968-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products ([Product_ID$]Integer,[Category_ID$] Integer,[Product_Name$]NVarchar(10),[Category_Name$] NVarchar(9),[Unit_Price$]FLOAT,[DW_DM_PRODUCTS_PRODUCTS_CKSUM$] INTEGER)

2009-03-05 17:21:42,984-0800 [Pool Worker—10] INFO—Bulk loading stagingtable ST_Products from source file Products.txt

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—BULK INSERTS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products FROM‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Products.txt.tmp’WITH (FORMATFILE=‘c:\SMI\Data \389a3dee-ce29-4e42-90ad-44970093f745\data\Products.txt.format’,FIRSTROW=1,ROWS_PER_BATCH=20,DATAFILETYPE=‘widechar’)

2009-03-05 17:21:42,984-0800 [Pool Worker—10] INFO—Successfully loadedST_Products

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Shopping_Carts] for 1, status Running

2009-03-05 17:21:42,984-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN: ST_Shopping_Carts]

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—Deleting formatfiles that might be lingering over from previous unsuccessfulrunprematurely terminated—Shopping Carts.txt.format.

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Shopping Carts.txt.format

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—Deleting tmp filesthat might be lingering over from previous unsuccessful runprematurelyterminated—Shopping Carts.txt.tmp.

2009-03-05 17:21:42,984-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Shopping Carts.txt.tmp

2009-03-05 17:21:42,984-0800 [Pool Worker—10] INFO—Preprocessing sourcefile Shopping Carts.txt(c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\ShoppingCarts.txt)

2009-03-05 17:21:43,312-0800 [Pool Worker—10] INFO—Read 3000 lines,wrote 3000 lines

2009-03-05 17:21:43,312-0800 [Pool Worker—10] INFO—Successfullypreprocessed source file Shopping Carts.txt

2009-03-05 17:21:43,343-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts ([Cart_ID$]Integer,[Store_ID$] Integer,[Loyalty_Card_ID$] Integer,[Sales_Date$]DateTime,[Sales_Date_Day_ID$]INTEGER,[DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$]INTEGER,[DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$]INTEGER,[DW_DM_STORES_STORES_CKSUM$] INTEGER)

2009-03-05 17:21:43,375-0800 [Pool Worker—10] INFO—Bulk loading stagingtable ST_Shopping_Carts from source file Shopping Carts.txt

2009-03-05 17:21:43,375-0800 [Pool Worker—10] DEBUG—BULK INSERTS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts FROM‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\ShoppingCarts.txt.tmp’ WITH(FORMATFILE=‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\ShoppingCarts.txt.format’,FIRSTROW=1,ROWS_PER_BATCH=3000,DATAFILETYPE=‘widechar’)

2009-03-05 17:21:43,406-0800 [Pool Worker—10] INFO—Successfully loadedST_Shopping_Carts

2009-03-05 17:21:43,406-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Stores] for 1, status Running

2009-03-05 17:21:43,406-0800 [Pool Worker—10] INFO—Starting LoadStaging[ACORN: ST_Stores]

2009-03-05 17:21:43,406-0800 [Pool Worker—10] DEBUG—Deleting formatfiles that might be lingering over from previous unsuccessfulrunprematurely terminated—Stores.txt.format.

2009-03-05 17:21:43,406-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Stores.txt.format

2009-03-05 17:21:43,406-0800 [Pool Worker—10] DEBUG—Deleting tmp filesthat might be lingering over from previous unsuccessful runprematurelyterminated—Stores.txt.tmp.

2009-03-05 17:21:43,406-0800 [Pool Worker—10] DEBUG—No files found withsearch pattern Stores.txt.tmp

2009-03-05 17:21:43,406-0800 [Pool Worker—10] INFO—Preprocessing sourcefile Stores.txt(c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Stores.txt)

2009-03-05 17:21:43,421-0800 [Pool Worker—10] INFO—Read 100 lines, wrote100 lines

2009-03-05 17:21:43,421-0800 [Pool Worker—10] INFO—Successfullypreprocessed source file Stores.txt

2009-03-05 17:21:43,453-0800 [Pool Worker—10] DEBUG—CREATE TABLES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores ([Store_ID$]Integer,[Region$] NVarchar(5),[City$] NVarchar(15),[Type$]NVarchar(11),[DW_DM_STORES_STORES_CKSUM$] INTEGER)

2009-03-05 17:21:43,468-0800 [Pool Worker—10] INFO—Bulk loading stagingtable ST_Stores from source file Stores.txt

2009-03-05 17:21:43,468-0800 [Pool Worker—10] DEBUG—BULK INSERTS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores FROM‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Stores.txt.tmp’WITH(FORMATFILE=‘c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data\Stores.txt.format’,FIRSTROW=1,ROWS_PER_BATCH=100,DATAFILETYPE=‘widechar’)

2009-03-05 17:21:43,468:0800 [Pool Worker—10] INFO—Successfully loadedST_Stores

2009-03-05 17:21:43,500-0800 [Pool Worker—10] INFO—Replacing unknownkeys in ST_Cart_Details

2009-03-05 17:21:43,531-0800 [Pool Worker—10] INFO—Replacing unknownkeys in ST_Customers

2009-03-05 17:21:43,546-0800 [Pool Worker—10] INFO—Replacing unknownkeys in ST_Products

2009-03-05 17:21:43,562-0800 [Pool Worker—10] INFO—Replacing unknownkeys in ST_Shopping_Carts

2009-03-05 17:21:43,578-0800 [Pool Worker—10] INFO—Replacing unknownkeys in ST_Stores

2009-03-05 17:21:43,828-0800 [Pool Worker—10] INFO—Updating checksums'

2009-03-05 17:21:43,828-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details SETDW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$=BINARY_CHECKSUM(Cart_ID$),DW_DM_PRODUCTS_PRODUCTS_CKSUM$=BINARY_CHECKSUM(Product_ID$),DW_DM_CART_DETAILS_CART_DETAILS_CKSUM$=BINARY_CHECKSUM(Cart_ID$,Product_ID$,Quantity$)

2009-03-05 17:21:43,875-0800 [Pool Worker—10] INFO—Updated 12438 rows

2009-03-05 17:21:43,875-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Cart_Details] for 1, status Complete

2009-03-05 17:21:43,875-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN: ST_Cart_Details]

2009-03-05 17:21:43,921-0800 [Pool Worker—10] INFO—Updating checksums

2009-03-05 17:21:43,921-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers SETDW_DM_CUSTOMERS_CUSTOMERS_CKSUM$=BINARY_CHECKSUM(Loyalty_Card_ID$,Age_Group$,Gender$)

2009-03-05 17:21:43,953-0800 [Pool Worker—10] INFO—Updated 100 rows

2009-03-05 17:21:43,953-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Customers] for 1, status Complete

2009-03-05 17:21:43,968-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN: ST_Customers]

2009-03-05 17:21:44,000-0800 [Pool Worker—10] INFO—Updating checksums

2009-03-05 17:21:44,015-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products SETDW_DM_PRODUCTS_PRODUCTS_CKSUM$=BINARY_CHECKSUM(Product_ID$,Category_ID$,Product_Name$,Category_Name$)

2009-03-05 17:21:44,015-0800 [Pool Worker—10] INFO—Updated 20 rows

2009-03-05 17:21:44,015-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Products] for 1, status Complete

2009-03-05 17:21:44,031-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN: ST_Products]

2009-03-05 17:21:44,062-0800 [Pool Worker—10] INFO—Updating checksums

2009-03-05 17:21:44,062-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts SETDW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$=BINARY_CHECKSUM(Cart_ID$,Store_ID$,Loyalty_Card_ID$,Sales_Date$),DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$=BINARY_CHECKSUM(Loyalty_Card_ID$),DW_DM_STORES_STORES_CKSUM$=BINARY_CHECKSUM(Store_ID$)

2009-03-05 17:21:44,093-0800 [Pool Worker—10] INFO—Updated 3000 rows

2009-03-05 17:21:44,093-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Shopping_Carts] for 1, status Complete

2009-03-05 17:21:44,125-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN: ST_Shopping_Carts]

2009-03-05 17:21:44,171-0800 [Pool Worker—10] INFO—Updating checksums

2009-03-05 17:21:44,171-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores SETDW_DM_STORES_STORES_CKSUM$=BINARY_CHECKSUM(Store_ID$,Region$,City$,Type$)

2009-03-05 17:21:44,671-0800 [Pool Worker—10] INFO—Updated 100 rows

2009-03-05 17:21:44,671-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN: ST_Stores] for 1, status Complete

2009-03-05 17:21:44,671-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN: ST_Stores]

2009-03-05 17:21:44,671-0800 [Pool Worker—10] DEBUG—Logging stepLoadStaging [ACORN] for 1, status Complete

2009-03-05 17:21:44,687-0800 [Pool Worker—10] INFO—Finished LoadStaging[ACORN]

2009-03-05 17:21:44,687-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 2 seconds for: LoadStagingc:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\data 1 loadgroup=ACORNdatabasepath=c:\SMI\Data\389a3dee-ce29-4e42-90ad-44970093f745\datanumrows=−1

2009-03-05 17:21:44,687-0800 [Pool Worker—10] INFO—Starting:LoadWarehouse 1 loadgroup=ACORN

2009-03-05 17:21:44,687-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN] for 1, status Running

2009-03-05 17:21:45,031-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN]

2009-03-05 17:21:45,031-0800 [Pool Worker—10] DEBUG—SELECT COUNT(*) FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details

2009-03-05 17:21:45,031-0800 [Pool Worker—10] DEBUG—SELECT COUNT(*) FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers

2009-03-05 17:21:45,031-0800 [Pool Worker—10] DEBUG—SELECT COUNT(*) FROMS_N389a3dee_ce29_4e42_90ad_449700931745.ST_Products

2009-03-05 17:21:45,031-0800 [Pool Worker—10] DEBUG—SELECT COUNT(*) FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts

2009-03-05 17:21:45,046-0800 [Pool Worker—10] DEBUG—SELECT COUNT(*) FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores

2009-03-05 17:21:45,046-0800 [Pool Worker—10] DEBUG—Order of loadingdimension tables: [Cart_Details Shopping_Carts, Cart_DetailsCart_Details, Customers Customers, Products Products, Stores Stores]

2009-03-05 17:21:45,046-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Shopping_Carts] for 1, status Running

2009-03-05 17:21:45,046-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Cart_Details Shopping_Carts]

2009-03-05 17:21:45,046-0800 [Pool Worker—10] INFO—Probing staging tableST_Cart_Details for DISTINCT to load Cart_Details Shopping_Carts

2009-03-05 17:21:45,046-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Cart_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

GROUP BY Cart_ID$

HAVING COUNT(*)_(>)1

2009-03-05 17:21:45,406-0800 [Pool Worker—10] INFO—Not distinct

2009-03-05 17:21:45,406-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Cart_Details Shopping_Carts from staging tableST_Cart_Details

2009-03-05 17:21:45,406-0800 [Pool Worker—10] DEBUG—INSERT

INTOS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS(Cart_ID$,ST_Shopping_Carts_CKSUM$,LOAD_ID,ST_Cart_Details_CKSUM$)

SELECT B.Cart_ID$,0,1, MAX(B.DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS        C    -   WHERE C.Cart_ID$=B.Cart_ID$)

GROUP BY B.Cart_ID$

2009-03-05 17:21:45,453-0800 [Pool Worker—10]INFO—[INSERTDT:1:Cart_Details Shopping_Carts:ST_Cart_Details:2976:[CartID]] 2976 rows inserted

2009-03-05 17:21:45,453-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Cart_Details Shopping_Carts

2009-03-05 17:21:45,453-0800 [Pool Worker—10] INFO—Probing staging tableST_Shopping_Carts for DISTINCT to load Cart_Details Shopping_Carts

2009-03-05 17:21:45,453-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Cart_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

GROUP BY Cart_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:45,562-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_SHOPPING_CARTSCART_DETAILSDCART_IDCART_DETAILSDST_SHOPPING_CARTS_CKSUMONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS(Cart_ID$,ST_Shopping_Carts_CKSUM$)

2009-03-05 17:21:45,828-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_ST_Shopping_CartsCART_DETAILSDCART_IDCART_DETAILSDDW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUMON S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts(Cart_ID$,DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$)

2009-03-05 17:21:46,031-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Cart_Details Shopping_Carts from staging tableST_Shopping_Carts

2009-03-05 17:21:46,031-0800 [Pool Worker—10] DEBUG—INSERT

INTOS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS(Cart_ID$,Store_ID$,Loyalty_Card_ID$,Sales_Date$,ST_Cart_Details_CKSUM$,LOAD_ID,ST_Shopping_Carts_CKSUM$)

SELECT B.Cart_ID$,B.Store_ID$,B.Loyalty_Card_ID$,B.Sales_Date$,0,1,B.DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS        C    -   WHERE C.Cart_ID$=B.Cart_ID$)

2009-03-05 17:21:46,046-0800 [Pool Worker—10]INFO—[INSERTDT:1:Cart_Details Shopping_Carts:ST_Shopping_Carts:24:[SalesDate, Loyalty Card ID, Cart ID, Store ID]] 24 rows inserted

2009-03-05 17:21:46,046-0800 [Pool Worker—10] INFO—Updating tableDW_DM_CART_DETAILS_SHOPPING_CARTS from staging table ST_Shopping_Carts

2009-03-05 17:21:46,046-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce294e4290ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTSSETLOAD_ID=1,Store_ID$=B.Store_ID$,Loyalty_Card_ID$=B.Loyalty_Card_ID$,Sales_Date$=B.Sales_Date$,ST_Shopping_Carts_CKSUM$=B.DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$

FROMS_N389a3dee_ce294e4290ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTSA

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts BON A.Cart_ID$=B.Cart_ID$ AND A.ST_Shopping_Carts_CKSUM$<>B.DW_DM_CART_DETAILS_SHOPPING_CARTS_CKSUM$

2009-03-05 17:21:46,250-0800 [Pool Worker—10]INFO—[UPDATEDT:1:Cart_Details Shopping_Carts: ST_Shopping_Carts:2976: [Sales Date, Loyalty Card ID, Store ID]] 2976 rows affected

2009-03-05 17:21:46,250-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Cart_Details Shopping_Carts

2009-03-05 17:21:46,250-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_SHOPPING_CARTSCART_DETAILSDSHOPPING_CARTS_13875573 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS(Shopping_Carts13875573$)

2009-03-05 17:21:46,328-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_SHOPPING_CARTSCART_DETAILSDCART_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS(Cart_ID$) INCLUDE (Shopping_Carts_13875573$)

2009-03-05 17:21:46,406-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Shopping_Carts] for 1, statusComplete

2009-03-05 17:21:46,437-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Cart_Details Shopping_Carts]

2009-03-05 17:21:46,437-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Cart_Details] for 1, status Running

2009-03-05 17:21:46,484-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Cart_Details Cart_Details]

2009-03-05 17:21:46,484-0800 [Pool Worker—10] INFO—Probing staging tableST_Cart_Details for DISTINCT to load Cart_Details Cart_Details

2009-03-05 17:21:46,484-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Cart_ID$,Product_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

GROUP BY Cart_ID$,Product_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:46,546-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_CART_DETAILSCART_DETAILSDCART_IDCART_DETAILSDPRODUCT_IDCART_DETAILSDST_CART_DETAILS_CKSUMONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS(Cart_ID$,Product_ID$,ST_Cart_Details_CKSUM$)

2009-03-05 17:21:46,578-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_ST_Cart_DetailsCART_DETAILSDCART_IDCART_DETAILSDPRODUCT_IDCART_DETAILSDDW_DM_CART_DETAILS_CART_DETAILS_CKSUMON S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details(Cart_ID$,Product_ID$,DW_DM_CART_DETAILS_CART_DETAILS_CKSUM$)

2009-03-05 17:21:46,781-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Cart_Details Cart_Details from staging table ST_Cart_Details

2009-03-05 17:21:46,781-0800 [Pool Worker—10] DEBUG—INSERT

INTOS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS(Cart_ID$,Product_ID$,Quantity$,LOAD_ID,ST_Cart_Details_CKSUM$)

SELECT B.Cart_ID$,B.Product_ID$,B.Quantity$,1,B.DW_DM_CART_DETAILS_CART_DETAILS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS        C    -   WHERE C.Cart_ID$=B.Cart_ID$ AND C.Product_ID$=B.Product_ID$)

2009-03-05 17:21:46,984-0800 [Pool Worker—10]INFO—[INSERTDT:1:Cart_DetailsCart_Details:ST_Cart_Details:12438:[Quantity, Cart ID, Product ID]]12438 rows inserted

2009-03-05 17:21:46,984-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Cart_Details Cart_Details

2009-03-05 17:21:46,984-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILSSETShopping_Carts_13875573$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS.Shopping_Carts_13875573$

FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTSONDW_DM_CART_DETAILS_CART_DETAILS.Cart_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS.Cart_ID$

WHERES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS.LOAD_ID=1

2009-03-05 17:21:47,156-0800 [Pool Worker—10] INFO—12438 rows updated

2009-03-05 17:21:47,156-0800 [Pool Worker—10] WARN—Unable to loaddimension table [Cart_Details Cart_Details] from staging table[ST_Shopping_Carts]: could not find columns in staging table that map toany dimension columns except for any potential references to naturalkeys

2009-03-05 17:21:47,156-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_CART_DETAILSCART_DETAILSDCART_DETAILS2003 51043 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS(Cart_Details200351043$) INCLUDE (Shopping_Carts_13875573$)

2009-03-05 17:21:47,265-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_CART_DETAILSCART_DETAILSDCART_IDCART_DETAILSDPRODUCT_IDONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS(Cart_ID$,Product_ID$) INCLUDE(Cart_Details200351043$,Shopping_Carts_13875573$)

2009-03-05 17:21:47,421-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CART_DETAILS_CART_DETAILSCART_DETAILSDCART_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS(Cart_ID$) INCLUDE (Cart_Details200351043$,Shopping_Carts_13875573$)

2009-03-05 17:21:47,515-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Cart_Details] for 1, status Complete

2009-03-05 17:21:47,531-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Cart_Details Cart_Details]

2009-03-05 17:21:47,531-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Customers Customers] for 1, status Running

2009-03-05 17:21:47,546-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Customers Customers]

2009-03-05 17:21:47,546-0800 [Pool Worker—10] WARN—Unable to loaddimension table [Customers Customers] from staging table[ST_Cart_Details]: natural keys not available at level Customers

2009-03-05 17:21:47,546-0800 [Pool Worker—10] INFO—Probing staging tableST_Shopping_Carts for DISTINCT to load Customers Customers

2009-03-05 17:21:47,546-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Loyalty_Card_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

GROUP BY Loyalty_Card_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:47,593-0800 [Pool Worker—10] INFO—Not distinct

2009-03-05 17:21:47,593-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Customers Customers from staging table ST_Shopping_Carts

2009-03-05 17:21:47,593-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS(Loyalty_Card_ID$,ST_Customers_CKSUM$,LOAD_ID,ST_Shopping_Carts_CKSUM$)

SELECT B.Loyalty_Card_ID$,0,1, MAX(B.DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS        C    -   WHERE C.Loyalty_Card_ID$=B.Loyalty_Card_ID$)

GROUP BY B.Loyalty_Card_ID$

2009-03-05 17:21:47,625-0800 [Pool Worker—10] INFO—[INSERTDT:1:CustomersCustomers:ST_Shopping_Carts:100:[Loyalty Card ID]] 100 rows inserted

2009-03-05 17:21:47,625-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Customers Customers

2009-03-05 17:21:47,625-0800 [Pool Worker—10] INFO—Probing staging tableST_Customers for DISTINCT to load Customers Customers

2009-03-05 17:21:47,625-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Loyalty_Card_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers B

GROUP BY Loyalty_Card_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:47,625-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_ST_CustomersCUSTOMERSDLOYALTY_CARD_IDCUSTOMERSDDW_DM_CUSTOMERS_CUSTOMERS_CKSUMON S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers(Loyalty_Card_ID$,DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$)

2009-03-05 17:21:47,640-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Customers Customers from staging table ST_Customers

2009-03-05 17:21:47,640-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS(Loyalty_Card_ID$,Age_Group$,Gender$,ST_Shopping_Carts_CKSUM$,LOAD_ID,ST_Customers_CKSUM$)

SELECT B.Loyalty_Card_ID$,B.Age_Group$,B.Gender$,0,1,B.DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS        C    -   WHERE C.Loyalty_Card_ID$.B.Loyalty_Card_ID$)

2009-03-05 17:21:47,656-0800 [Pool Worker—10] INFO—[INSERTDT:1:CustomersCustomers:ST_Customers:0:[Loyalty Card ID, Age Group, Gender]] 0 rowsinserted

2009-03-05 17:21:47,656-0800 [Pool Worker—10] INFO—Updating tableDW_DM_CUSTOMERS_CUSTOMERS from staging table ST_Customers

2009-03-05 17:21:47,656-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS SETLOAD_ID=1,Age_Group$=B.Age_Group$,Gender$=B.Gender$,ST_Customers_CKSUM$=BDW_DM_CUSTOMERS_CUSTOMERS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS A

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers B ONA.Loyalty_Card_ID$=B.Loyalty_Card_ID$ AND A.ST_Customers_CKSUM$<>B.DW_DM_CUSTOMERS_CUSTOMERS_CKSUM$

2009-03-05 17:21:47,703-0800 [Pool Worker—10] INFO—[UPDATEDT:1:CustomersCustomers:ST_Customers:100:[Age Group, Gender]] 100 rows affected

2009-03-05 17:21:47,703-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Customers Customers

2009-03-05 17:21:47,703-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_CUSTOMERS_CUSTOMERSCUSTOMERSDCUSTOMERS120094747 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS(Customers120094747$)

2009-03-05 17:21:47,734-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Customers Customers] for 1, status Complete

2009-03-05 17:21:47,734-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Customers Customers]

2009-03-05 17:21:47,734-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Products Products] for 1, status Running

2009-03-05 17:21:47,734-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Products Products]

2009-03-05 17:21:47,734-0800 [Pool Worker—10] INFO—Probing staging tableST_Cart_Details for DISTINCT to load Products Products

2009-03-05 17:21:47,734-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Product_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

GROUP BY Product_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:47,750-0800 [Pool Worker—10] INFO—Not distinct

2009-03-05 17:21:47,750-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Products Products from staging table ST_Cart_Details

2009-03-05 17:21:47,750-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS(Product_ID$,ST_Products_CKSUM$,LOAD_ID,ST_Cart_Details_CKSUM$)

SELECT B.Product_ID$,0,1, MAX(B.DW_DM_PRODUCTS_PRODUCTS_CKSUM$)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS        C    -   WHERE C.Product_ID$.B.Product_ID$)

GROUP BY B.Product_ID$

2009-03-05 17:21:47,765-0800 [Pool Worker—10] INFO—[INSERTDT:1:ProductsProducts:ST_Cart_Details:20:[Product ID]] 20 rows inserted

2009-03-05 17:21:47,765-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Products Products

2009-03-05 17:21:47,765-0800 [Pool Worker—10] INFO—Probing staging tableST_Products for DISTINCT to load Products Products

2009-03-05 17:21:47,765-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Product_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products B

GROUP BY Product_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:47,843-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_ST_ProductsPRODUCTSDPRODUCT_IDPRODUCTSDDW_DM_PRODUCTS_PRODUCTS_CKSUMON S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products(Product_ID$,DW_DM_PRODUCTS_PRODUCTS_CKSUM$)

2009-03-05 17:21:47,890-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Products Products from staging table ST_Products

2009-03-05 17:21:47,890-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS(Product_ID$,Category_ID$,Product_Name$,Category_Name$,ST_Cart_Details_CKSUM$,LOAD_ID,ST_Products_CKSUM$)

SELECTB.Product_ID$,B.Category_ID$,B.Product_Name$,B.Category_Name$,0,1,B.DW_DM_PRODUCTS_PRODUCTS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM        S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS        C    -   WHERE C.Product_IDS=B.Product_ID$)

2009-03-05 17:21:47,890-0800 [Pool Worker—10] INFO—[INSERTDT:1:ProductsProducts:ST_Products:0:[Product Name, Category ID, Category Name,Product ID]] 0 rows inserted

2009-03-05 17:21:47,890-0800 [Pool Worker—10] INFO—Updating tableDW_DM_PRODUCTS_PRODUCTS from staging table ST_Products

2009-03-05 17:21:47,890-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS SETLOAD_ID=1,Category_ID$=B.Category_ID$,Product_Name$=B.Product_Name$,Category_Name$=B.Category_Name$,ST_Products_CKSUM$=B.DW_DM_PRODUCTS_PRODUCTS_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS A

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products B ONA.Product_ID$.B.Product_ID$ AND A.ST_Products_CKSUM$<>B.DW_DM_PRODUCTS_PRODUCTS_CKSUM$

2009-03-05 17:21:47,906-0800 [Pool Worker—10] INFO—[UPDATEDT:1:ProductsProducts:ST_Products:20:[Product Name, Category ID, Category Name]] 20rows affected

2009-03-05 17:21:47,906-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Products Products

2009-03-05 17:21:47,906-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_PRODUCTS_PRODUCTSPRODUCTSDPRODUCTS1249892458 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS(Products1249892458$)

2009-03-05 17:21:47,937-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Products Products] for 1, status Complete

2009-03-05 17:21:47,937-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Products Products]

2009-03-05 17:21:47,937-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Stores Stores] for 1, status Running

2009-03-05 17:21:47,937-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Stores Stores]

2009-03-05 17:21:47,937-0800 [Pool Worker—10] WARN—Unable to loaddimension table [Stores Stores] from staging table [ST_Cart_Details]:natural keys not available at level Stores

2009-03-05 17:21:47,937-0800 [Pool Worker—10] INFO—Probing staging tableST_Shopping_Carts for DISTINCT to load Stores Stores

2009-03-05 17:21:47,937-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Store_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

GROUP BY Store_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:47,937-0800 [Pool Worker—10] INFO—Not distinct

2009-03-05 17:21:47,937-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Stores Stores from staging table ST_Shopping_Carts

2009-03-05 17:21:47,937-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES(Store_ID$,ST_Stores_CKSUM$,LOAD_ID,ST_Shopping_Carts_CKSUM$)

SELECT B.Store_ID$,0,1, MAX(B.DW_DM_STORES_STORES_CKSUM$)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES        C    -   WHERE C.Store_ID$=B.Store_ID$)

GROUP BY B.Store_ID$

2009-03-05 17:21:47,984-0800 [Pool Worker—10] INFO—[INSERTDT:1:StoresStores:ST_Shopping_Carts:100:[Store ID]] 100 rows inserted

2009-03-05 17:21:47,984-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Stores Stores

2009-03-05 17:21:47,984-0800 [Pool Worker—10] INFO—Probing staging tableST_Stores for DISTINCT to load Stores Stores

2009-03-05 17:21:47,984-0800 [Pool Worker—10] DEBUG—

SELECT TOP 1 Store_ID$,COUNT(*)

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores B

GROUP BY Store_ID$

HAVING COUNT(*)>1

2009-03-05 17:21:48,015-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_ST_StoresSTORESDSTORE_IDSTORESDDW_DM_STORES_STORES_CKSUM ONS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores(Store_ID$,DW_DM_STORES_STORES_CKSUM$)

2009-03-05 17:21:48,031-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Stores Stores from staging table ST_Stores

2009-03-05 17:21:48,031-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES(Store_ID$,Region$,City$,Type$,ST_Shopping_Carts_CKSUM$,LOAD_ID,ST_Stores_CKSUM$)

SELECT B.Store_ID$,B.Region$,B.City$,B.Type$,0,1,B.DW_DM_STORES_STORES_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores B

WHERE NOT EXISTS

-   -   (SELECT *    -   FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES        C    -   WHERE C.Store_ID$=B.Store_ID$)

2009-03-05 17:21:48,046-0800 [Pool Worker—10] INFO—[INSERTDT:1:StoresStores:ST_Stores:0:[Region, Type, Store ID, City]] 0 rows inserted

2009-03-05 17:21:48,046-0800 [Pool Worker—10] INFO—Updating tableDW_DM_STORES_STORES from staging table ST_Stores

2009-03-05 17:21:48,046-0800 [Pool Worker—10] DEBUG—UPDATES_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES SETLOAD_ID=1,Region$=B.Region$,City$=B.City$,Type$=B.Type$,ST_Stores_CKSUM$=B.DW_DM_STORES_STORES_CKSUM$

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES A

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores B ONA.Store_ID$=B.Store_ID$ AND A.ST_Stores_CKSUM$<>B.DW_DM_STORES_STORES_CKSUM$

2009-03-05 17:21:48,093-0800 [Pool Worker—10] INFO—[UPDATEDT:1:StoresStores:ST_Stores:100:[Region, Type, City]] 100 rows affected

2009-03-05 17:21:48,093-0800 [Pool Worker—10] INFO—Updating surrogatekeys for load id 1 in dimension table Stores Stores

2009-03-05 17:21:48,093-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_DM_STORES_STORESSTORESDSTORES1543357431 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES(Stores1543357431$)

2009-03-05 17:21:48,140-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Stores Stores] for 1, status Complete

2009-03-05 17:21:48,140-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Stores Stores]

2009-03-05 17:21:48,140-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Day Products Customers Stores Fact]for 1, status Running

2009-03-05 17:21:48,140-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Cart_Details Day Products Customers Stores Fact]

2009-03-05 17:21:48,140-0800 [Pool Worker—10] INFO—Deleting any recordsfrom table Cart_Details Day Products Customers Stores Fact with sameload ID

2009-03-05 17:21:48,140-0800 [Pool Worker—10] DEBUG—DELETE

FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES

WHERE LOAD_ID=1

2009-03-05 17:21:48,156-0800 [Pool Worker—10] INFO—0 rows deleted

2009-03-05 17:21:48,156-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Cart_Details Day Products Customers Stores Fact from stagingtable ST_Cart_Details

2009-03-05 17:21:48,156-0800 [Pool Worker—10] DEBUG—INSERT

INTOS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES(DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Cart_Details$Cart_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Cart_Details$Product_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Products$Product_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Time$Day_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Cart_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Product_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Quantity$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Cart_Details$Shopping_Carts_13875573$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Cart_Details$Cart_Details200351043$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Products$Products1249892458$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESTime$Week_ID$,Time$Month_ID$,Time$Quarter_ID$,DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Stores$Stores1543357431$,DW_SF_CARTDETAILS_DAY_PRODUCTS_CUSTOMERS_STORES.Customers$Customers120094747$,Stores$Store_ID$,Time$Sales_Date_Day_ID$,Customers$Loyalty_Card_ID$,LOAD_ID)

SELECTB.Cart_ID$,B.Product_ID$,B.Product_ID$,39870,B.Cart_ID$,B.Product_ID$,B.Quantity$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS.Shopping_Carts_13875573$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS.Cart_Details200351043$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS.Products1249892458$,dbo.DW_DM_TIME_DAY.Week_(—)ID$,dbo.DW_DM_TIME_DAY.Month_ID$,dbo.DW_DM_TIME_DAY.Quarter_ID$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Stores1543357431$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Customers120094747$,ST_Shopping_Carts$.Store_ID$,ST_Shopping_Carts$Sales_Date_Day_ID$,ST_Shopping_Carts$.Loyalty_Card_ID$,1

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Cart_Details B

LEFT OUTER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_CartsST_Shopping_Carts$ ON B.Cart_ID$=ST_Shopping_Carts$.Cart_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTSONB.Cart_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS.Cart_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILSONB.Cart_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS.Cart_ID$ ANDB.Product_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_CART_DETAILS.Product_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS ONB.Product_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS.Product_ID$

INNER JOIN dbo.DW_DM_TIME_DAY ON 39870=dbo.DW_DM_TIME_DAY.Day_ID$

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORESONST_Shopping_Carts$.Store_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Store_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS ONST_Shopping_Carts$.Loyalty_Card_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Loyalty_Card_ID$

2009-03-05 17:21:48,343-0800 [Pool Worker—10]INFO—[INSERTF:1:Cart_Details Day Products Customers StoresFact:ST_Cart_Details:12438:[Quantity, Cart ID, Product ID]] 12438 rowsinserted

2009-03-05 17:21:48,343-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESCUSTOMERSDCUSTOMERS120094747ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES(Customers$Customers120094747$)

2009-03-05 17:21:48,421-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESPRODUCTSDPRODUCTS1249892458ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Products$Products1249892458$)

2009-03-05 17:21:48,453-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESTIMEDDAY_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Time$Day_ID$)

2009-03-05 17:21:48,468-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESTIMEDWEEK_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES(Time$Week_ID$)

2009-03-05 17:21:48,515-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESTIMEDMONTH_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Time$Month_ID$)

2009-03-05 17:21:48,546-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESTIMEDQUARTER_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Time$Quarter_ID$)

2009-03-05 17:21:48,562-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESSTORESDSTORES1543357431ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES(Stores$Stores1543357431$)

2009-03-05 17:21:48,609-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESCART_DETAILSDCART_DETAILS200351043ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Cart_Details$Cart_Details200351043$)

2009-03-05 17:21:48,656-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESCART_DETAILSDSHOPPING_CARTS43875573ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTSCUSTOMERS_STORES (Cart_Details$Shopping_Carts_13875573$)

2009-03-05 17:21:48,671-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORESLOAD_ID ON

S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CART_DETAILS_DAY_PRODUCTS_CUSTOMERS_STORES(LOAD_ID)

2009-03-05 17:21:48,734-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Cart_Details Day Products Customers Stores Fact]for 1, status Complete

2009-03-05 17:21:48,734-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Cart_Details Day Products Customers Stores Fact]

2009-03-05 17:21:48,734-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Shopping_Carts Day Customers Stores Fact] for 1,status Running

2009-03-05 17:21:48,734-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Shopping_Carts Day Customers Stores Fact]

2009-03-05 17:21:48,734-0800 [Pool Worker—10] INFO—Deleting any recordsfrom table Shopping_Carts Day Customers Stores Fact with same load ID

2009-03-05 17:21:48,734-0800 [Pool Worker—10] DEBUG—DELETE

FROMS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES

WHERE LOAD_ID=1

2009-03-05 17:21:48,750-0800 [Pool Worker—10] INFO—0 rows deleted

2009-03-05 17:21:48,750-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Shopping_Carts Day Customers Stores Fact from staging tableST_Shopping_Carts

2009-03-05 17:21:48,750-0800 [Pool Worker—10] DEBUG—INSERT

INTOS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESDW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Cart_Details$Cart_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Customers$Loyalty_Card_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Stores$Store_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Time$Day_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Cart_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Loyalty_Card_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Store_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Time$Sales_Date_Day_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Cart_Details$Shopping_Carts_13875573$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Time$Week_ID$,Time$Month_ID$,Time$Quarter_ID$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Stores$Stores1543357431$,DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES.Customers$Customers120094747$,LOAD_ID)

SELECTB.Cart_ID$,B.Loyalty_Card_ID$,B.Store_ID$,39870,B.Cart_ID$,B.Loyalty_Card_ID$,B.Store_ID$,B.Sales_Date_Day_ID$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CARTDETAILS_SHOPPING_CARTS.Shopping_Carts_13875573$,dbo.DW_DM_TIME_DAY.Week_ID$,dbo.DW_DM_TIME_DAY.Month_ID$,dbo.DW_DM_TIME_DAY.Quarter_ID$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Stores1543357431$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Customers120094747$,1

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Shopping_Carts B

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTSONB.Cart_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CART_DETAILS_SHOPPING_CARTS.Cart_ID$

INNER JOIN dbo.DW_DM_TIME_DAY ON 39870=dbo.DW_DM_TIME_DAY.Day_ID$

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORESONB.Store_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Store_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_449700931745.DW_DM_CUSTOMERS_CUSTOMERS ONB.Loyalty_Card_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Loyalty_Card_ID$

2009-03-05 17:21:48,796-0800 [Pool Worker—10]INFO—[INSERTF:1:Shopping_Carts Day Customers StoresFact:ST_Shopping_Carts:3000:[Loyalty Card ID, Cart ID, Store ID]] 3000rows inserted

2009-03-05 17:21:48,796-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESCUSTOMERSDCUSTOMERS120094747ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Customers$Customers120094747$)

2009-03-05 17:21:48,828-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESTIMEDDAY_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Time$Day_ID$)

2009-03-05 17:21:48,843-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESTIMEDWEEK_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Time$Week_ID$)

2009-03-05 17:21:48,890-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESTIMEDMONTH_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Time$Month_ID$)

2009-03-05 17:21:48,921-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESTIMEDQUARTER_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Time$Quarter_ID$)

2009-03-05 17:21:48,921-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESSTORESDSTORES15433 57431 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Stores$Stores1543357431$)

2009-03-05 17:21:48,953-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESCART_DETAILSDSHOPPING_CARTS_13875573ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(Cart_Details$Shopping_Carts_13875573$)

2009-03-05 17:21:48,968-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORESLOAD_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_SHOPPING_CARTS_DAY_CUSTOMERS_STORES(LOAD_ID)

2009-03-05 17:21:48,984-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Shopping_Carts Day Customers Stores Fact] for 1,status Complete

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Shopping_Carts Day Customers Stores Fact]

2009-03-05 17:21:48,984-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Products Day Fact] for 1, status Running

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Products Day Fact]

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—Deleting any recordsfrom table Products Day Fact with same load ID

2009-03-05 17:21:48,984-0800 [Pool Worker—10] DEBUG—DELETE

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY

WHERE LOAD_ID=1

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—0 rows deleted

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Products Day Fact from staging table ST_Products

2009-03-05 17:21:48,984-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(DW_SF_PRODUCTS_DAY.Products$Product_ID$,DW_SF_PRODUCTS_DAY.Time$Day_ID$,DW_SF_PRODUCTS_DAY.Product_ID$,DW_SF_PRODUCTS_DAY.Category_ID$,DW_SF_PRODUCTS_DAY.Unit_Price$,DW_SF_PRODUCTS_DAY.Products$Products1249892458$,DW_SF_PRODUCTS_DAY.Time$Week_ID$,Time$Month_ID$,Time$Quarter_ID$,LOAD_ID)

SELECTB.Product_ID$,39870,B.Product_ID$,B.Category_ID$,B.Unit_Price$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS.Products1249892458$,dbo.DW_DM_TIME_DAY.Week_ID$,dbo.DW_DM_TIME_DAY.Month_ID$,dbo.DW_DM_TIME_DAY.Quarter_ID$,1

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Products B

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS ONB.Product_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_PRODUCTS_PRODUCTS.Product_ID$

INNER JOIN dbo.DW_DM_TIME_DAY ON 39870=dbo.DW_DM_TIME_DAY.Day_ID$

2009-03-05 17:21:48,984-0800 [Pool Worker—10] INFO—[INSERTF:1:ProductsDay Fact:ST_Products:20:[Category ID, Unit Price, Product ID]] 20 rowsinserted

2009-03-05 17:21:49,000-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_PRODUCTS_DAYTIMEDDAY_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(Time$Day_ID$)

2009-03-05 17:21:49,015-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_PRODUCTS_DAYTIMEDWEEK_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(Time$Week_ID$)

2009-03-05 17:21:49,015-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_PRODUCTS_DAYTIMEDMONTH_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(Time$Month_ID$)

2009-03-05 17:21:49,031-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_PRODUCTS_DAYTIMEDQUARTER_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(Time$Quarter_ID$)

2009-03-05 17:21:49,031-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_PRODUCTS_DAYPRODUCTSDPRODUCTS1249892458 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY(Products$Products1249892458$)

2009-03-05 17:21:49,062-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_SF_PRODUCTS_DAYLOAD_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_PRODUCTS_DAY (LOAD_ID)

2009-03-05 17:21:49,062-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Products Day Fact] for 1, status Complete

2009-03-05 17:21:49,062-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Products Day Fact]

2009-03-05 17:21:49,078-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Customers Day Fact] for 1, status Running

2009-03-05 17:21:49,078-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Customers Day Fact]

2009-03-05 17:21:49,078-0800 [Pool Worker—10] INFO—Deleting any recordsfrom table Customers Day Fact with same load ID

2009-03-05 17:21:49,078-0800 [Pool Worker—10] DEBUG—DELETE

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY

WHERE LOAD_ID=1

2009-03-05 17:21:49,093-0800 [Pool Worker—10] INFO—0 rows deleted

2009-03-05 17:21:49,093-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Customers Day Fact from staging table ST_Customers

2009-03-05 17:21:49,093-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAYDW_SF_CUSTOMERS_DAY.Customers$Loyalty_Card_ID$,DW_SF_CUSTOMERS_DAY.Time$Day_ID$,DW_SF_CUSTOMERS_DAY.Loyalty_Card_ID$,DW_SF_CUSTOMERS_DAY.Time$Week_ID$,Time$Month_ID$,Time$Quarter_ID$,DW_SF_CUSTOMERS_DAY.Customers$Customers120094747$,LOAD_ID)

SELECTB.Loyalty_Card_ID$,39870,B.Loyalty_Card_ID$,dbo.DW_DM_TIME_DAY.Week_ID$,dbo.DW_DM_TIME_DAY.Month_ID$,dbo.DW_DM_TIME_DAY.Quarter_ID$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Customers120094747$,1

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Customers B

INNER JOIN dbo.DW_DM_TIME_DAY ON 39870=dbo.DW_DM_TIME_DAY.Day_ID$

INNER JOINS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS ONB.Loyalty_Card_D$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_CUSTOMERS_CUSTOMERS.Loyalty_Card_ID$

2009-03-05 17:21:49,093-0800 [Pool Worker—10] INFO—[INSERTF:1:CustomersDay Fact:ST_Customers:100:[Loyalty Card ID]] 100 rows inserted

2009-03-05 17:21:49,093-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CUSTOMERS_DAYCUSTOMERSDCUSTOMERS120094747 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY(Customers$Customers120094747$)

2009-03-05 17:21:49,140-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CUSTOMERS_DAYTIMEDDAY_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY(Time$Day_ID$)

2009-03-05 17:21:49,140-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CUSTOMERS_DAYTIMEDWEEK_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY(Time$Week_ID$)

2009-03-05 17:21:49,140-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CUSTOMERS_DAYTIMEDMONTH_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY(Time$Month_ID$)

2009-03-05 17:21:49,171-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_CUSTOMERS_DAYTIMEDQUARTER_ID ONS_N389a3dee_ce29_4e42_90ad_44970093045.DW_SF_CUSTOMERS_DAY(Time$Quarter_ID$)

2009-03-05 17:21:49,187-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_SF_CUSTOMERS_DAYLOAD_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_CUSTOMERS_DAY (LOAD_ID)

2009-03-05 17:21:49,187-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Customers Day Fact] for 1, status Complete

2009-03-05 17:21:49,187-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Customers Day Fact]

2009-03-05 17:21:49,187-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Stores Day Fact] for 1, status Running

2009-03-05 17:21:49,187-0800 [Pool Worker—10] INFO—StartingLoadWarehouse [ACORN: Stores Day Fact]

2009-03-05 17:21:49,187-0800 [Pool Worker—10] INFO—Deleting any recordsfrom table Stores Day Fact with same load ID

2009-03-05 17:21:49,187-0800 [Pool Worker—10] DEBUG—DELETE

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY

WHERE LOAD_ID=1

2009-03-05 17:21:49,187-0800 [Pool Worker—10] INFO—0 rows deleted

2009-03-05 17:21:49,187-0800 [Pool Worker—10] INFO—Inserting new recordsinto table Stores Day Fact from staging table ST_Stores

2009-03-05 17:21:49,187-0800 [Pool Worker—10] DEBUG—INSERT

INTO S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY(DW_SF_STORES_DAY.Stores$Store_ID$,DW_SF_STORES_DAY.Time$Day_ID$,DW_SF_STORES_DAY.Store_ID$,DW_SF_STORES_DAY.Time$Week_ID$,Time$Month_ID$,Time$Quarter_ID$,DW_SF_STORES_DAY.Stores$Stores1543357431$,LOAD_ID)

SELECTB.Store_ID$,39870,B.Store_ID$,dbo.DW_DM_TIME_DAY.Week_ID$,dbo.DW_DM_TIME_DAY.Month_ID$,dbo.DW_DM_TIME_DAY.Quarter_ID$,S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Stores1543357431$,1

FROM S_N389a3dee_ce29_4e42_90ad_44970093f745.ST_Stores B

INNER JOIN dbo.DW_DM_TIME_DAY ON 39870=dbo.DW_DM_TIME_DAY.Day_ID$

INNER JOIN S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORESONB.Store_ID$=S_N389a3dee_ce29_4e42_90ad_44970093f745.DW_DM_STORES_STORES.Store_ID$

2009-03-05 17:21:49,203-0800 [Pool Worker—10] INFO—[INSERTF:1:Stores DayFact:ST_Stores:100:[Store ID]] 100 rows inserted

2009-03-05 17:21:49,203-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_STORES_DAYTIMEDDAY_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY (Time$Day_ID$)

2009-03-05 17:21:49,218-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_STORES_DAYTIMEDWEEK_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY (Time$Week_ID$)

2009-03-05 17:21:49,234-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_STORES_DAYTIMEDMONTH_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY(Time$Month_ID$)

2009-03-05 17:21:49,234-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_STORES_DAYTIMEDQUARTER_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY(Time$Quarter_ID$)

2009-03-05 17:21:49,234-0800 [Pool Worker—10] DEBUG—CREATE INDEXMX_DW_SF_STORES_DAYSTORESDSTORES1543357431 ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY(Stores$Stores1543357431$)

2009-03-05 17:21:49,281-0800 [Pool Worker—10] DEBUG—CREATE INDEXDX_DW_SF_STORES_DAYLOAD_ID ONS_N389a3dee_ce29_4e42_90ad_44970093f745.DW_SF_STORES_DAY (LOAD_ID)

2009-03-05 17:21:49,312-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN: Stores Day Fact] for 1, status Complete

2009-03-05 17:21:49,312-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN: Stores Day Fact]

2009-03-05 17:21:49,312-0800 [Pool Worker—10] DEBUG—Logging stepLoadWarehouse [ACORN] for 1, status Complete

2009-03-05 17:21:49,328-0800 [Pool Worker—10] INFO—FinishedLoadWarehouse [ACORN]

2009-03-05 17:21:49,328-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 4 seconds for: LoadWarehouse 1 loadgroup=ACORN

2009-03-05 17:21:49,328-0800 [Pool Worker—10] INFO—Starting:ExecuteScriptGroup ACORN

2009-03-05 17:21:49,328-0800 [Pool Worker—10] WARN—Empty script group:There are no scripts defined in script group ACORN

2009-03-05 17:21:49,328-0800 [Pool Worker—10] INFO—Elapsed Time=0minutes, 0 seconds for: ExecuteScriptGroup ACORN

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A system, comprising: one or more processorsconfigured to: receive a first data set, the first data set including aplurality of tables and a plurality of keys; automatically identify aset of metric-attribute combinations, wherein a metric-attributecombination includes at least one metric and at least one attribute, andwherein the set of metric-attribute combinations is automaticallyidentified at least in part by: extracting metrics from the first dataset; evaluating the extracted metrics to determine which metrics havethe least uniform statistical distribution; for a portion of theextracted metrics determined to have the least uniform statisticaldistribution, evaluating attributes associated with the portion of theextracted metrics determined to have the least uniform distribution toselect one or more combinations of metrics and attributes that arelikely to be of interest to a user, wherein evaluating a candidatemetric-attribute combination includes: determining an information gainof an attribute relative to a metric, wherein attributes with a higherinformation gain relative to the metric are determined to be more likelyto be of interest to the user than attributes having a lower informationgain; prioritize the identified set of metric-attribute combinations atleast in part by ranking the identified set of metric-attributecombinations based at least in part on information gain; andautomatically generate one or more dashboard reports for the user atleast in part by using a portion of the prioritized metric-attributecombinations, wherein the portion of the prioritized metric-attributecombinations used to generate the one or more dashboard reports includesthe metric-attribute combinations prioritized highest based at least inpart on information gain; and a memory coupled to the one or moreprocessors and configured to provide the one or more processors withinstructions.
 2. The system of claim 1 wherein the metric-attributecombination includes a plurality of attributes.
 3. The system of claim 1wherein the metric-attribute combination includes a plurality ofattributes.
 4. The system of claim 1 wherein the one or more processorsare further configured to map a dimensional model onto the first dataset.
 5. A method, comprising: receiving a first data set, the first dataset including a plurality of tables and a plurality of keys;automatically identifying, using one or more processors, a set ofmetric-attribute combinations, wherein a metric-attribute combinationincludes at least one metric and at least one attribute, and wherein theset of metric-attribute combinations is automatically identified atleast in part by: extracting metrics from the first data set; evaluatingthe extracted metrics to determine which metrics have the least uniformstatistical distribution; for a portion of the extracted metricsdetermined to have the least uniform statistical distribution,evaluating attributes associated with the portion of the extractedmetrics determined to have the least uniform distribution to select oneor more combinations of metrics and attributes that are likely to be ofinterest to a user, wherein evaluating a candidate metric-attributecombination includes: determining an information gain of an attributerelative to a metric, wherein attributes with a higher information gainrelative to the metric are determined to be more likely to be ofinterest to the user than attributes having a lower information gain;prioritizing the identified set of metric-attribute combinations atleast in part by ranking the identified set of metric-attributecombinations based at least in part on information gain; andautomatically generating one or more dashboard reports for the user atleast in part by using a portion of the prioritized metric-attributecombinations, wherein the portion of the prioritized metric-attributecombinations used to generate the one or more dashboard reports includesthe metric-attribute combinations prioritized highest based at least inpart on information gain.
 6. The method of claim 5 wherein themetric-attribute combination includes a plurality of metrics.
 7. Themethod of claim 5 wherein the metric-attribute combination includes aplurality of attributes.
 8. A computer program product embodied in acomputer readable storage medium and comprising computer instructionsfor: receiving a first data set, the first data set including aplurality of tables and a plurality of keys; automatically identifying,using one or more processors, a set of metric-attribute combinations,wherein a metric-attribute combination includes at least one metric andat least one attribute, and wherein the set of metric-attributecombinations is automatically identified at least in part by: extractingmetrics from the first data set; evaluating the extracted metrics todetermine which metrics have the least uniform statistical distribution;for a portion of the extracted metrics determined to have the leastuniform statistical distribution, evaluating attributes associated withthe portion of the extracted metrics determined to have the leastuniform distribution to select one or more combinations of metrics andattributes that are likely to be of interest to a user, whereinevaluating a candidate metric-attribute combination includes:determining an information gain of an attribute relative to a metric,wherein attributes with a higher information gain relative to the metricare determined to be more likely to be of interest to the user thanattributes having a lower information gain; prioritizing the identifiedset of metric-attribute combinations at least in part by ranking theidentified set of metric-attribute combinations based at least in parton information gain; and automatically generating one or more dashboardreports for the user at least in part by using a portion of theprioritized metric-attribute combinations, wherein the portion of theprioritized metric-attribute combinations used to generate the one ormore dashboard reports includes the metric-attribute combinationsprioritized highest based at least in part on information gain.
 9. Thesystem of claim 1, wherein the at least one attribute comprises adimensional attribute upon which the at least one metric is grouped orsliced.
 10. The system of claim 1 wherein receiving the first data setincludes ingesting uploaded source data.